Leiden Coloring Algorithm for Influencer Detection
Handrizal Handrizal, Poltak Sihombing, Erna Budhiarti Nababan, Mohammad Andri Budiman · International Journal of Advanced Computer Science and Applications · 2024
In today's digital age, the role of influencers, especially on social media platforms, has grown significantly. A commonly used feature by business professionals today is follower grouping. However, this feature is limited to identifying influencers based solely on mutual followership, highlighting the need for a more sophisticated approach to influencer detection. This study proposes a novel method for influencer detection that integrates the Leiden coloring algorithm and Degree centrality. This approach leverages network analysis to identify patterns and relationships within large-scale datasets. Initially, the Leiden coloring algorithm is employed to partition the network into various communities, considered potential influencer hubs. Subsequently, Degree centrality is utilized to identify nodes with high connectivity, indicating influential individuals. The proposed method was validated using data crawled from Twitter (X) social media, employing the keyword "GarudaIndonesia." The data was collected using Tweet-Harvest between January 1, 2020, and October 16, 2024, resulting in a dataset of 22,623 rows. The dataset was subjected to two experimental scenarios: 1,000 and 5,000 rows. Compared to the Louvain coloring method, the proposed approach demonstrated an increase in the modularity value of the Leiden coloring algorithm by 0.0306, a reduction in time processing by 14.4848 seconds, and a decrease in the number of communities by 1,290.